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Electronics
Special Issues
Machine and Deep Learning Applications to Computer Vision and Recognition
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Machine and Deep Learning Applications to Computer Vision and Recognition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1033

Special Issue Editors

Dr. Wenxiao Wang

E-Mail Website
Guest Editor
School of Software Technology, Zhejiang University, Hangzhou 310058, China
Interests: machine learning; deep learning; computer vision
Dr. Zihang Jiang

E-Mail Website
Guest Editor
School of Biomedical Engineering, USTC, Suzhou Institute for Advanced Research, Suzhou 215100, China
Interests: deep learning; computer vision; multimodal learning
Dr. Cai Xu

E-Mail Website
Guest Editor
School of Computer Science and Technology, Xidian University, Xi’an 710071, China
Interests: trustworthy machine learning; multi-modal learning; image retrieval
Special Issues, Collections and Topics in MDPI journals
Dr. Yuan Li

E-Mail Website
Guest Editor
School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China
Interests: deep learning; computer vision

Special Issue Information

Dear Colleagues,

We aim to showcase cutting-edge research on the application of machine learning and deep learning technologies in the fields of computer vision and pattern recognition. As visual perception advances, machine learning and deep learning algorithms are revolutionizing how images and videos are processed, generated, and understood.

This Special Issue will focus on topics such as visual perception, image/video generation, image retrieval, edge computing for vision tasks, and multimodal learning, encouraging innovation in both theory and applications. Key areas of interest include using deep neural networks and generative models to extract high-level semantic information from visual data and improve performance in complex environments. We also wish to explore the advancements in edge computing, particularly in performing visual tasks efficiently in resource-constrained settings and in multimodal learning, integrating data from multiple sources to enhance model performance.

We invite researchers from academia and industry to submit original research, reviews, or practical applications related to these topics, contributing to the further development of computer vision and recognition systems.

Dr. Wenxiao Wang
Dr. Zihang Jiang
Dr. Cai Xu
Dr. Yuan Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • deep learning
  • visual perception
  • image/video generation
  • image retrieval
  • edge computing for vision tasks
  • multimodal learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

17 pages, 395 KiB  
Article
WePred: Edge Weight-Guided Contrastive Learning for Bipartite Link Prediction
by Linlin Ding, Yiming Han, Mo Li, Yinghao Gu, Tingting Liu and Shidong Yu
Electronics 2025, 14(1), 20; https://doi.org/10.3390/electronics14010020 - 25 Dec 2024
Viewed by 723
Abstract
Bipartite networks are common in real-world applications, where link prediction helps understand network evolution and make recommendations. Traditional methods have two major limitations; they often ignore edge weight information by treating links as binary connections, and they struggle to capture complex interaction patterns [...] Read more.
Bipartite networks are common in real-world applications, where link prediction helps understand network evolution and make recommendations. Traditional methods have two major limitations; they often ignore edge weight information by treating links as binary connections, and they struggle to capture complex interaction patterns in sparse networks. We propose WePred, a weight-guided contrastive learning method for link prediction which addresses both challenges through three key components: (1) a weight-guided edge attention mechanism that incorporates edge weights into neighbor aggregation using dynamic attention scores, enabling the fine-grained capture of interaction strengths; (2) a dual-level contrastive learning approach that combines edge-level and node-level contrasts to capture both local weighted patterns and global structural dependencies, which is particularly effective in sparse regions; and (3) a unified learning framework that integrates classification and contrastive objectives. We evaluate WePred on five real-world datasets. The experimental results show that WePred consistently outperformed state-of-the-art methods, achieving improvements of 7.27% in the AUC on ML-1M and 3.18% in the AUC on the sparsest dataset of Amazon-Book (density: 0.001%). Ablation studies confirm the effectiveness of each component, while parameter analyses provide deployment guidance. Full article
(This article belongs to the Special Issue Machine and Deep Learning Applications to Computer Vision and Recognition)
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